Algorithm 3. Pseudocode of GXGBoot’s steps.
1.	Initialization:
2.	mutation_rate = 0.1 //Mutation rate for GA
3.	min_mutation_momentum = 0.0001 //Min mutation momentum
4.	max_mutation_momentum = 0.1 //Max mutation momentum
5.	min_population = 5 //Min population for GA
6.	max_population = 10 //Max population for GA
7.	num_Iterations = 10 //Number of iterations to evaluate GA
8.	Input:
9.	Training Set, Validation Set
10.	Begin
11.	num_population = random.randint (min_population, max_population); // Generate initial population for GXGBoost
12.	population_GXGBoost = [[]
13.	For i in range (num_population):
14.	GXGBoost_parameters = random.randint (min_num_estimators, max_num_estimators) // GXGBoost parameters generation
15.	GXGBoost_ model = generate_ GXGBoost (GXGBoost_parameters)
16.	population_GXGBoost.append (GXGBoost_ model)
17.	End for
18.	max_accuracy = 0
19.	best_model = None
20.	population_validation_accuracy= [[]
21.	For i in range (num_Iterations):
22.	For j in range (num_population):
23.	GXGBoost_model = population_GXGBoost [j] // population selection // population evaluation
24.	validation_accuracy = evaluate_ GXGBoost (GXGBoost_model, Training_Set, Validation_Set)
25.	population_validation_accuracy.append (validation_accuracy)
26.	If validation_accuracy > max_accuracy:
27.	max_accuracy = validation_accuracy
28.	best_model = GXGBoost_model
29.	End if
30.	End for
31.	// Create new population with new generations
32.	# every generation will use the current best GXGBoost_model to mate
33.	For pop_index in range (num_population):
34.	model1 = population_GXGBoost [pop_index]
35.	model1_validation_accuracy = population_validation_accuracy [pop_index]
36.	model2 = best_model
37.	model2_validation_accuracy= max_accuracy
38.	// Create new generation with crossover
39.	new_model = crossover_GXGBoost (model1, model1_validation_accuracy, model2, model2_validation_accuracy)
40.	mutate_GXGBoost (new_model) // Mutate new generation
41.	population_GXGBoost [pop_index] = new_model // Replace current model
42.	End for
43.	End for
44.	Return best_model, max_accuracy
45.	End